Saliency-based Multiple Region of Interest Detection from a Single 360deg image

360deg images are informative -- it contains omnidirectional visual information around the camera. However, the areas that cover a 360deg image is much larger than the human's field of view, therefore important information in different view directions is easily overlooked. To tackle this issue, we propose a method for predicting the optimal set of Region of Interest (RoI) from a single 360deg image using the visual saliency as a clue. To deal with the scarce, strongly biased training data of existing single 360deg image saliency prediction dataset, we also propose a data augmentation method based on the spherical random data rotation. From the predicted saliency map and redundant candidate regions, we obtain the optimal set of RoIs considering both the saliency within a region and the Interaction-Over-Union (IoU) between regions. We conduct the subjective evaluation to show that the proposed method can select regions that properly summarize the input 360deg image.